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在智力障碍者中使用心率和呼吸变化的自动睡眠分期
N van den Broek1, F van Meulen1,2, M Ross3
1Centre for Sleep Medicine, Kempenhaeghe, Heeze, The Netherlands.
Journal of intellectual disability research : JIDR
|June 9, 2023
概括
分析心率和呼吸变异性可以准确估计智力障碍 (ID) 患者的睡眠阶段. 这为这种人群更好的睡眠监测提供了一种不那么侵入性的多睡眠学 (PSG) 替代方案.
科学领域:
- 睡眠医学 睡眠医学
- 生物医学工程 生物医学工程
- 关于残疾人的研究.
背景情况:
- 智力障碍 (ID) 个体的睡眠障碍的患病率更高.
- 睡眠诊断的黄金标准是多睡眠学 (PSG),由于传感器负担和睡眠障碍,在ID患者中存在挑战.
- 对这一群体来说,研究不那么侵入性睡眠评估方法至关重要.
研究的目的:
- 评估分析心率变化和呼吸变化的有效性,以自动评分ID患者的睡眠阶段.
- 确定心肺呼吸睡眠分期 (CReSS) 算法是否可以在这个人群中提供可靠的睡眠阶段评估.
主要方法:
- 对比了CReSS算法的睡眠阶段得分与手动得分的PSG数据,这些数据来自73名ID患者.
- 使用心电图 (ECG),呼吸力或两者的组合作为CReSS算法的输入.
- 通过使用科恩的卡帕系数来评估协议,并探索人口统计和并发症的影响.
主要成果:
- 使用结合心电图和呼吸力度的CReSS算法显示,与手动PSG评分的最佳一致性 (kappa = 0.62).
- 虽然和手动评分困难对协议产生了负面影响,但算法的性能仍然可以接受.
- 在没有的ID患者中,CReSS的表现接近于一般睡眠障碍患者中观察到的表现.
结论:
- 心率和呼吸变异性分析是估计ID患者睡眠阶段的可行方法.
- 这种方法对开发不那么侵入性睡眠监测设备充满希望,例如可穿戴设备,专为ID患者量身定制.
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